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From "George Porter (JIRA)" <j...@apache.org>
Subject [jira] Updated: (HADOOP-4801) DFS read performance suboptimal when client co-located on nodes with data
Date Sat, 13 Dec 2008 03:52:44 GMT

     [ https://issues.apache.org/jira/browse/HADOOP-4801?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel

George Porter updated HADOOP-4801:

    Attachment: HADOOP-4801.1.patch

This patch contains a proof-of-concept implementation of "HDFS Direct-I/O", which is a mechanism
for HDFS clients to directly access datablocks residing on the local host.  It does this by
bypassing the standard HDFS datablock streaming protocol to the local DataNode, and instead
locates and opens the raw datablocks directly.

The changes are mostly contained to DFSClient.java.  BlockReader is now an interface, and
there are two implementing classes: RemoteBlockReader and DirectBlockReader.  RemoteBlockReader
is the baseline, working exactly as before.  DirectBlockReader instances are created in blockSeekTo(long).
 A check is made to see if the requested offset resides in a local block on the same machine
as the DFSClient.  If so, then the DirectBlockReader opens that file and passes through any
I/O operations directly to the Java filesystem layer.

I performed two main sets of performance testing: a streaming test and a random read test.
 Both tests were performed on a single machine running the Hadoop trunk code in the pseudodistributed
mode (with 1 datanode, 1 namenode, and dfs.replication set to 1 and the default block size).

In the streaming test, I opened a 1GB file and read it from start to finish into memory.
  Baseline: 8.730 seconds with std. dev of 0.052
  DirectIO: 5.266 seconds with std. dev of 0.116

In the random test, I opened a 1GB or 4GB file, then performed a set of random reads, by picking
a random offset in the file, seeking to that offset, and reading 1KB of data.

For the 1 GB file
  Baseline with 1024 reads: 861 reads/second
  DirectIO with 1024 reads: 5988 reads/second

  Baseline with 4096 reads: 1065 reads/second
  DirectIO with 4096 reads: 9287 reads/second

For the 4 GB file
  Baseline with 65,535 reads: 535 reads/second
  DirectIO with 65,535 reads: 17,852 reads/second

It's hard to tell how much these results are affected by various disk caches, etc., and so
I wanted to put this patch out there to get your experiences with it.  Obviously local block
read performance will only improve application performance to the extent that you read from
locally resident disk blocks.

Your feedback appreciated!  Thanks.

> DFS read performance suboptimal when client co-located on nodes with data
> -------------------------------------------------------------------------
>                 Key: HADOOP-4801
>                 URL: https://issues.apache.org/jira/browse/HADOOP-4801
>             Project: Hadoop Core
>          Issue Type: Improvement
>          Components: dfs
>    Affects Versions: 0.19.0
>            Reporter: George Porter
>         Attachments: HADOOP-4801.1.patch
> One of the major strategies Hadoop uses to get scalable data processing is to move the
code to the data.  However, putting the DFS client on the same physical node as the data blocks
it acts on doesn't improve read performance as much as expected.
> After looking at Hadoop and O/S traces (via HADOOP-4049), I think the problem is due
to the HDFS streaming protocol causing many more read I/O operations (iops) than necessary.
 Consider the case of a DFSClient fetching a 64 MB disk block from the DataNode process (running
in a separate JVM) running on the same machine.  The DataNode will satisfy the single disk
block request by sending data back to the HDFS client in 64-KB chunks.  In BlockSender.java,
this is done in the sendChunk() method, relying on Java's transferTo() method.  Depending
on the host O/S and JVM implementation, transferTo() is implemented as either a sendfilev()
syscall or a pair of mmap() and write().  In either case, each chunk is read from the disk
by issuing a separate I/O operation for each chunk.  The result is that the single request
for a 64-MB block ends up hitting the disk as over a thousand smaller requests for 64-KB each.
> Since the DFSClient runs in a different JVM and process than the DataNode, shuttling
data from the disk to the DFSClient also results in context switches each time network packets
get sent (in this case, the 64-kb chunk turns into a large number of 1500 byte packet send
operations).  Thus we see a large number of context switches for each block send operation.
> I'd like to get some feedback on the best way to address this, but I think providing
a mechanism for a DFSClient to directly open data blocks that happen to be on the same machine.
 It could do this by examining the set of LocatedBlocks returned by the NameNode, marking
those that should be resident on the local host.  Since the DataNode and DFSClient (probably)
share the same hadoop configuration, the DFSClient should be able to find the files holding
the block data, and it could directly open them and send data back to the client.  This would
avoid the context switches imposed by the network layer, and would allow for much larger read
buffers than 64KB, which should reduce the number of iops imposed by each read block operation.

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